To demonstrate, or not to demonstrate?

As the atomic bomb was becoming a technological reality, there were many scientists on the Manhattan Projectwho found themselves wondering about both the ethics and politics of a surprise, unwarned nuclear attack on a city. Many of them, even at very high levels, wondered about whether the very threat of the bomb, properly displayed, might be enough, without the loss of life that would come with a military attack.

…
the way in which nuclear weapons, now secretly developed in this country, will first be revealed to the world appears of great, perhaps fateful importance.
…It will be very difficult to persuade the world that a nation which was capable of secretly preparing and suddenly releasing a weapon, as indiscriminate as the rocket bomb and a thousand times more destructive, is to be trusted in its proclaimed desire of having such weapons abolished by international agreement….

From this point of view a demonstration of the new weapon may best be made before the eyes of representatives of all United Nations, on the desert or a barren island.
The best possible atmosphere for the achievement of an international agreement could be achieved if America would be able to say to the world,
“You see what weapon we had but did not use. We are ready to renounce its use in the future and to join other nations in working out adequate supervision of the use of this nuclear weapon.”

They even went so far as to suggest, in a line that was
until recently
totally etched out of thehistorical record by the Manhattan Project censors, that
“We fear its early unannounced use might cause other nations to regard us as a nascent Germany.”

The evolution of the “Trinity” test fireball, at constant scale, with the Empire State Building for additional scale reference.

The idea of a “demonstration” was for many scientists a compelling one, and news of the ideaspread to the various project sites. The idea would be to let the Japanese know what awaited them if they did not surrender. This would be more than just a verbal or textual warning, which could be disregarded as propaganda — they would set the bomb off somewhere where casualties would be low or minimal, but its nature easy to verify. If the demonstration did not work, if the Japanese were not receptive, then the bomb could be used as before. In the eyes of these scientists, there would be no serious loss to do it this way, and perhaps much to gain.

Of course, not all scientists saw it this way. In his
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forwarding the Franck Report to the Secretary of War, the physicist Arthur Compton, head of the Chicago laboratory, noted his own doubts: 1. if it didn’t work, it would be prolonging the war, which would cost lives; and 2. “without a military demonstration it may be impossible to impress the world with the need for national sacrifices in order to gain lasting security.” This last line is the more interesting one in my eyes: Compton saw dropping the bomb on a city as a
form
of “demonstration,” a “military demonstration,” and thought that taking a lot of life now would be necessary to scare the world into banning these weapons in the future. This view, that the bombs were something more than just weapons, butvisual arguments, comes across in
otherscientists’ discussions of targeting questions as well
.

We’ve said it numerous times and we’re going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there’s no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better withbinary values, other with continuous, so it is important to know how to treat your data properly.

Preprocessing needs to be used with caution and understanding of your data to avoid losing important information or, worse, overfitting the model. A good example is a case of paramedics, who usually don’t record pulse if it is normal. Missing values here thus cannot be imputed by an average value or random number, but as a distinct value (normal pulse). Domain knowledge is always crucial for data preparation.

Widget no. 2: Discretize

For certain tasks you might want to resort to binning, which is what
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does. It effectively distributes your continuous values into a selected number of bins, thus making the variable discrete-like. You can either discretize all your data variablesat once, using selected discretization type, or select a particular discretization methodfor each attribute. The cool thing is the transformation is already displayed in the widget, so you instantly know what you’re getting in the end. A good example of discretization would be having a data set of your customers with their age recorded. It would make little sense to segment customers by each particular age, so binning them into 4 age groups (young, young-adult, middle-aged, senior) would be a great solution. Also some visualizations require feature transformation –
Sieve Diagram
is currently one such widget. Mosaic Display, however, has the transformation already implemented internally.

Get a broom and sort your data! That’s what
Purge Domain
does. If all of the values of some attributes areconstant, it will removethese attributes. If you have unused (empty) attributes in your data, it will remove them. Effectively, you will get a nice and comprehensive data set in the end.

Of course, don’t forget to include all these procedures into your report with the ‘Report’ button!

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An analysis plan or strategy should be developed before any analysis is undertaken to ensure that the modelling is hypothesis-led rather than data-driven. The
a priori
model-fitting analysis strategy should identify:

the covariates which are to be considered for inclusion in any modelling approach to analysis

the order in which confounding variables are to be considered for inclusion in the model with the intervention variable fitted last (or an ‘intervention × phase’ interaction if pre- and post-measurements have been taken).
14

An example of a model-fitting analysis strategy which could have been used for the URGE data is displayed in Figure 1.

Multilevel modelling was undertaken for the URGE study using the software package MLWin, developed by the Institute of Education in London (Table 2). As outlined above, an
a priori
model-fitting analysis strategy was developed which identified the order in which covariates were to be included in the model. Only after all covariates were included in the model was the effect of the ‘intervention × phase’ interaction examined. After adjustment for the pre-identified covariates, the interaction remained significant. The effect size estimated from the multilevel model was 0.70 (95% CI: 0.55–0.91). The resulting
t
-ratio was
t
= 2.71,
P
= 0.01. This indicates that when all the data are used in the analysis, the waiting time was on average 30% less in the guideline group compared with the control group (Table 2).

An in-depth discussion of all the available modelling methods is beyond the scope of this article. Researchers should refer to specific texts such as Murray
2
for a general introduction to possible methods, or to Kreft and de Leeuw
15
for discussion of multilevel models. Similarly, a range of statistical software packages are available for the analysis of clustered data sets. A discussion of the more common packages can be found on the multilevel modelling web site:
http://www.ioe.ac.uk/multilevel/
. For a discussion of generalized estimating equations, readers should refer to Burton
et al.
16

These modelling techniques adjust well for clustering and allow adjustment for both cluster level and patient level covariates. These types of analyses are more computationally intensive, however, and require greater statistical expertise both in the execution of the procedures and in the interpretation of the results.

With the increasing popularity of the cluster randomized trial, it is important that researchers be aware of the implications of adopting such a design. Cluster RCTs are more complex to undertake than patient randomized trials in that they require increased sample sizes, with associated recruitment issues, and the analysis of these trials is not so straightforward. Cluster trials are the gold standard design for some interventions, however, and it is important that researchers have the information to design and analyse them appropriately.